Experimental Methodology for Prescription Maps of Variable Rate Nitrogenous Fertilizers on Cereal Crops

Donvito, Angelo R.; D'Antonio, Paola; Lopinto, S.

doi:10.1007/978-3-030-39299-4_93

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…As vegetative biomass is the commercial product of alfalfa, a map of biomass corresponds to a map of forage yield in this crop. Yield maps are crucial tools for driving precision farming operations [ 34 ], and given the high predictive value of hus over biomass (R 2 = 0.81 for fresh and 0.84 for dry mass, Figure 7 a,e), maps of ultrasound-determined height can be considered proxies of yield maps and used for driving delineation of uniform management zones in spatially-aware farming [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Monitoring Plant Height and Spatial Distribution of Biometrics with a Low-Cost Proximal Platform

Bitella,

Bochicchio,

Castronuovo

et al. 2024

Plants

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Measuring canopy height is important for phenotyping as it has been identified as the most relevant parameter for the fast determination of plant mass and carbon stock, as well as crop responses and their spatial variability. In this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode. The tool was lab-tested and field-tested on crop systems of different geometry and spacings: in a static setting on faba bean (Vicia faba L.) and in an on-the-go setting on chia (Salvia hispanica L.), alfalfa (Medicago sativa L.), and wheat (Triticum durum Desf.). Cross-correlation (CC) or a dynamic time-warping algorithm (DTW) was used to analyze and correct shifts between manual and sensor data in chia. Sensor data were able to reproduce with minor shifts in canopy profile and plant status indicators in the field when plant heights varied gradually in narrow-spaced chia (R2 = 0.98), faba bean (R2 = 0.96), and wheat (R2 = up to 0.99). Abrupt height changes resulted in systematic errors in height estimation, and short-scale variations were not well reproduced (e.g., R2 in widely spaced chia was 0.57 to 0.66 after shifting based on CC or DTW, respectively)). In alfalfa, ultrasound data were a better predictor than NDVI (Normalized Difference Vegetation Index) for Leaf Area Index and biomass (R2 from 0.81 to 0.84). Maps of ultrasound-determined height showed that clusters were useful for spatial management. The good performance of the tool both in a static setting and in the on-the-go setting provides flexibility for the determination of plant height and spatial variation of plant responses in different conditions from natural to managed systems.

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Section: Discussionmentioning

confidence: 99%

Monitoring Plant Height and Spatial Distribution of Biometrics with a Low-Cost Proximal Platform

Bitella,

Bochicchio,

Castronuovo

et al. 2024

Plants

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“…As reported in previous studies, the topographic wetness index (TWI) is an attribute of flood occurrences. It measures the degree of moisture in a watershed under the influence of gravity [45,46]. In the present study, the TWI was calculated using the following equation [45]:…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Hitouri,

Mohajane,

Lahsaini

et al. 2024

Remote Sensing

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Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.

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“…In addition, fodder crops mechanized operations usually performed with wet soils are responsible for the degradation of the physical soil structure. The agronomic management of agricultural fields carried out uniformly, with spatial variability evident, is economically and environmentally inefficient [4][5][6]. The alternative is to adopt innovative resilient farming systems matching conventional tillage yields, facing the current climatechanging and production costs scenarios [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…The alternative is to adopt innovative resilient farming systems matching conventional tillage yields, facing the current climatechanging and production costs scenarios [7,8]. For this reason, site-specific management is important for enhancing crop productivity and increasing nutrient use efficiency [5,9].…”

Section: Introductionmentioning

confidence: 99%

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

Conceição,

Silva,

Valero

et al. 2024

AgriEngineering

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In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

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Experimental Methodology for Prescription Maps of Variable Rate Nitrogenous Fertilizers on Cereal Crops

Cited by 6 publications

References 23 publications

Monitoring Plant Height and Spatial Distribution of Biometrics with a Low-Cost Proximal Platform

Monitoring Plant Height and Spatial Distribution of Biometrics with a Low-Cost Proximal Platform

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

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